A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship

By Jack Weatheritt, Richard D. Sandberg. Journal of Computational Physics, 2016

309 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ae561a5669b9eb2f9910bfae7cccce7e5ea602b0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Heng Xiao
2. Steven L. Brunton
3. Ricardo Vinuesa
4. Paola Cinnella
5. Jin-Long Wu
6. Karthik Duraisamy
7. Gianluca Iaccarino
8. Eric Paterson

In Statistical and Nonlinear Physics

1. Ricardo Vinuesa
2. Steven L. Brunton
3. Heng Xiao
4. Paola Cinnella
5. Karthik Duraisamy
6. Jin-Long Wu
7. Eric Paterson
8. Gianluca Iaccarino

In Statistics and Probability

1. Heng Xiao
2. Ricardo Vinuesa
3. Steven L. Brunton
4. Paola Cinnella
5. Jin-Long Wu
6. Karthik Duraisamy
7. Eric Paterson
8. Gianluca Iaccarino

Citations in the corpus

• Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “When it comes to large-scale datasets, gene expression programming (GEP), for example, has been applied to recover turbulence closures [376].”
• Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot (2018, methodology): “For the specifics of tailoring the algorithm to turbulence modeling, see Weatheritt and Sandberg’s initial implementation [7].”
• Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework (2017, methodology): “Summarized briefly, three distinctly different approaches to data-driven turbulence modeling have been pursued by different groups: (i) Weatheritt and Sandsberg [16, 17] used Gene Expression Programming to develop algebraic Reynolds stress models based on symbolic regression.”
• Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2018, unknown): “…challenges in data-driven Reynolds stress closures Recently, data-driven turbulence modeling has emerged as a promising field of research with a number of approaches been proposed in the past few years (e.g., Ling et al. 2016; Singh & Duraisamy 2016; Wang et al. 2017; Weatheritt & Sandberg 2016).”
• Turbulence Modeling in the Age of Data (2018, methodology): “In a related effort Weatheritt and Sandberg (2016, 2017) used symbolic regression and gene expression programming for defining the coefficients c ( θ , η ) in the context of algebraic Reynolds stress models, resulting in an explicit model form that can be readily implemented in RANS solvers.”
• Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “Note that the classification omitted data-driven methods that primarily focused on developing turbulence models [e.g., 46, 47] rather than quantifying their uncertainties.”
• Enhancing computational fluid dynamics with machine learning (2021, methodology): “The Reynolds-stress anisotropy tensor was also modelled by Weatheritt and Sandberg [58], who performed tests in RANS simulations of turbulent ducts.”